Generative Adversarial Networks (GANs)

Lists

Variants of GAN structure

Results for mnist

Network architecture of generator and discriminator is the exaclty sames as in infoGAN paper.
For fair comparison of core ideas in all gan variants, all implementations for network architecture are kept same except EBGAN and BEGAN. Small modification is made for EBGAN/BEGAN, since those adopt auto-encoder strucutre for discriminator. But I tried to keep the capacity of discirminator.

Random generation

Conditional generation

Each row has the same noise vector and each column has the same label condition.

Name

Epoch 1

Epoch 10

Epoch 25

CGAN

ACGAN

infoGAN

InfoGAN : Manipulating two continous codes

Results for fashion-mnist

Comments on network architecture in mnist are also applied to here.Fashion-mnist is a recently proposed dataset consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. (T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot)

Random generation

Conditional generation

Each row has the same noise vector and each column has the same label condition.

Name

Epoch 1

Epoch 20

Epoch 40

CGAN

ACGAN

infoGAN

Without hyper-parameter tuning from mnist-version, ACGAN/infoGAN does not work well as compared with CGAN.
ACGAN tends to fall into mode-collapse.
infoGAN tends to ignore noise-vector. It results in that various style within the same class can not be represented.

Random generation

All results are randomly sampled.

Name

Epoch 1

Epoch 10

Epoch 25

VAE

GAN

Results of GAN is also given to compare images generated from VAE and GAN.
The main difference (VAE generates smooth and blurry images, otherwise GAN generates sharp and artifact images) is cleary observed from the results.

Conditional generation

Each row has the same noise vector and each column has the same label condition.

Name

Epoch 1

Epoch 10

Epoch 25

CVAE

CGAN

Results of CGAN is also given to compare images generated from CVAE and CGAN.